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  Kernel Methods and Their Applications to Signal Processing

Bousquet, O., & Perez-Cruz, F. (2003). Kernel Methods and Their Applications to Signal Processing. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03) (pp. 860-863). Piscataway, NJ, USA: IEEE. doi:10.1109/ICASSP.2003.1202779.

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Bousquet, O1, 2, Author              
Perez-Cruz, F, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Recently introduced in Machine Learning, the notion of kernels has drawn a lot of interest as it allows to obtain non-linear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the Support Vector Machines has produced significant progress. The successes of such algorithms is now spreading as they are applied to more and more domains. Many Signal Processing problems, by their non-linear and high-dimensional nature may benefit from such techniques. We give an overview of kernel methods and their recent applications.

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 Dates: 2003-06
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2018
DOI: 10.1109/ICASSP.2003.1202779
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Title: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003)
Place of Event: Hong Kong, China
Start-/End Date: 2003-04-06 - 2003-04-10

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Title: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: 4 Sequence Number: - Start / End Page: 860 - 863 Identifier: ISBN: 0-7803-7663-3